Our team of three developed an engaging two-player checkers game featuring a clean user interface and an intelligent AI opponent. Built using Python with pygame and tkinter, the system handles animation, player interaction, and strategic AI decision-making.
Designed and implemented a simulated Major League Baseball database consisting of over 14 entities and 8 complex relationships with thousands of generated records for analysis and querying.
Leveraged Python, NetworkX, and OpenAI's GPT-3.5 to extract dialogue and construct a large-scale character interaction network for visual and analytical exploration.
Developed a full-stack fantasy football application that integrates with ESPN’s Fantasy Football API to retrieve, cache, and manage league and player data. Implemented Python data ingestion and storage layer to reduce external API calls, improve performance, and mitigate rate-limiting risks. Designed and built front end with React featuring comprehensive historical analytics, including player statistics, league leaderboards, and trend analysis. Focused on efficient data handling, scalability, and an intuitive user experience to support advanced fantasy football insights.
Designed and developed a vehicle maintenance tracking application that allows users to add, edit, and delete vehicles while maintaining detailed service histories for each. Implemented automated oil change reminders along with upcoming and overdue maintenance alerts to support proactive vehicle care. Enabled export of maintenance records to CSV for reporting and data portability. Delivered a simple, clean user interface focused on usability, clarity, and efficient day-to-day tracking.